Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-09T07:42:24.136Z Has data issue: false hasContentIssue false

Merging Graphics and Text to Better Convey Experimental Results: Designing an “Enhanced Bar Graph”

Published online by Cambridge University Press:  12 June 2017

William D. Berry
Affiliation:
Florida State University
Matthew Hauenstein
Affiliation:
Florida State University

Abstract

We propose a format for presenting experimental results that combines a graph’s strength in facilitating general-pattern recognition with a table’s strength in displaying numerical results. The format supplements a conventional bar graph with additional text labels and graphics but also can be based on a dot plot. The resulting enhanced bar graph conveys general patterns about treatment effects; displays point estimates and confidence intervals for all key quantities of interest relevant to testing hypotheses (e.g., first differences in the mean of the dependent variable); and clarifies the interpretation of these quantities as treatment effects. Presenting information in a single figure avoids the need to devote scarce journal space to both a graph and a table. Moreover, an enhanced bar graph prevents readers from having to move back and forth between a graph and a table of numerical results—thereby reducing their cognitive load and facilitating their understanding of the findings.

Type
Articles
Copyright
Copyright © American Political Science Association 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Al-Ubaydli, Omar, McCabe, Kevin, and Twieg, Peter. 2014. “Can More Be Less? An Experimental Test of the Resource Curse.” Journal of Experimental Political Science 1: 3958.Google Scholar
Broockman, David E. 2014. “Mobilizing Candidates: Political Actors Strategically Shape the Candidate Pool with Personal Appeals.” Journal of Experimental Political Science 1: 104–19.Google Scholar
Chandler, Paul and Sweller, John. 1992. “The Split-Attention Effect as a Factor in the Design of Instruction.” British Journal of Educational Psychology 62: 233–46.Google Scholar
Cleveland, William S. 1984. “Graphical Methods for Data Presentation: Full-Scale Breaks, Dot Charts, and Multibased Logging.” The American Statistician 38: 270–80.Google Scholar
Druckman, James N., Green, Donald P., Kuklinski, James H., and Lupia, Arthur. 2006. “The Growth and Development of Experimental Research in Political Science.” American Political Science Review 100: 627–36.Google Scholar
Gelman, Andrew, Pasarica, Cristian, and Dodhia, Rahul. 2002. “Let’s Practice What We Preach: Turning Tables into Graphs.” The American Statistician 56: 121–30.CrossRefGoogle Scholar
Gillan, Douglas J., Wickens, Christopher D., Hollands, J. G., and Melody Carswell, C.. 1998. “Guidelines for Presenting Quantitative Data in HFES Publications.” Human Factors 40: 2841.Google Scholar
Healy, Andrew, Kuo, Alexander G., and Malhotra, Neil. 2014. “Partisan Bias in Blame Attribution: When Does It Occur?” Journal of Experimental Political Science 1: 144–58.CrossRefGoogle Scholar
Hink, Jessica K., Wogalter, Michael S., and Eustace, Jason K.. 1996. “Display of Quantitative Information: Are Grables Better than Plain Graphs or Tables?” Proceedings of the Human Factors and Ergonomics Society, 40th Annual Meeting, 1155–9.Google Scholar
Jacoby, William G. 2006. “The Dot Plot: A Graphical Display for Labeled Quantitative Values.” The Political Methodologist 14: 614.Google Scholar
Jacoby, William G. and Schneider, Saundra. 2010. “Graphical Displays for Political Science Journal Articles.” Paper presented at the Visions in Methodology Conference, Iowa City, IA, March.Google Scholar
Kastellec, Jonathan P. and Leoni, Eduardo L.. 2007. “Using Graphs Instead of Tables in Political Science.” Perspectives on Politics 5: 755–71.Google Scholar
Kosslyn, Stephen Michael. 2006. Graph Design for the Eye and Mind. Cary, NC: Oxford University Press.Google Scholar
Krupnikov, Yanna and Levine, Adam Seth. 2014. “Cross-Sample Comparisons and External Validity.” Journal of Experimental Political Science 1: 5980.Google Scholar
Lane, David M. and Sándor, Anikó. 2009. “Designing Better Graphs by Including Distributional Information and Integrating Words, Numbers, and Images.” Psychological Methods 14: 239–57.Google Scholar
Mironova, Vera and Whitt, Sam. 2014. “Ethnicity and Altruism after Violence: The Contact Hypothesis in Kosovo.” Journal of Experimental Political Science 1: 170–80.Google Scholar
Stadelmann, David, Portmann, Marco, and Eichenberger, Reiner. 2014. “Full Transparency of Politicians’ Actions Does Not Increase the Quality of Political Representation.” Journal of Experimental Political Science 1: 1623.Google Scholar
Sweller, John, Chandler, Paul, Tierney, Paul, and Cooper, Martin. 1990. “Cognitive Load as a Factor in the Structuring of Technical Material.” Journal of Experimental Psychology: General 119: 176–92.Google Scholar
Wainer, Howard. 1997. “Improving Tabular Displays, with NAEP Tables as Examples and Inspirations.” Journal of Educational and Behavioral Statistics 22: 130.Google Scholar
Supplementary material: PDF

Berry and Hauenstein supplementary material

Online Appendix

Download Berry and Hauenstein supplementary material(PDF)
PDF 1 MB